1/f Noise and Machine Intelligence in a Nonlinear Dopant Atom Network. Issue 3 (15th January 2021) - Chen, Tao; Bobbert, Peter A.; van der Wiel, Wilfred G.
Abstract : Noise exists in nearly all physical systems ranging from simple electronic devices such as transistors to complex systems such as neural networks. To understand a system's behavior, it is vital to know the origin of the noise and its characteristics. Recently, it was shown that the nonlinear electronic properties of a disordered dopant atom network in silicon can be exploited for efficiently executing classification tasks through "material learning." Here, we study the dopant network's intrinsic 1/ f noise arising from Coulomb interactions, and its impact on the features that determine its computational abilities, viz., the nonlinearity and the signal‐to‐noise ratio (SNR), is investigated. The findings on optimal SNR and nonlinear transformation of data by this nonlinear network provide a guideline for the scaling of physical learning machines and shed light on neuroscience from a new perspective. Abstract : 1/ f noise is found to be fundamentally coupled with the machine intelligence of a dopant network in silicon. The network's 1/ f noise and machine intelligence both originate from its internal nonlinear interactions. Rather than attempting to minimize the noise, as in traditional electronics, the dopant network is balanced between its signal‐to‐noise‐ratio and information processing capability, at optimal bias conditions.